Detailed Information

Cited 2 time in webofscience Cited 2 time in scopus
Metadata Downloads

General Overview of Artificial Intelligence for Interstitial Cystitis in Urologyopen access

Authors
Cho, YongwonPark, Jong MokYoun, Seunghyun
Issue Date
Nov-2023
Publisher
KOREAN CONTINENCE SOC
Keywords
Interstitial cystitis; Diagnosis; Treatment; Convolutional neural network; Deep learning; Large language model
Citation
International Neurourology Journal, v.27, pp S64 - S72
Indexed
SCIE
SCOPUS
KCI
Journal Title
International Neurourology Journal
Volume
27
Start Page
S64
End Page
S72
URI
https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/64951
DOI
10.5213/inj.2346294.147
ISSN
2093-4777
2093-6931
Abstract
Our understanding of interstitial cystitis/bladder pain syndrome (IC/BPS) has evolved over time. The diagnosis of IC/BPS is primarily based on symptoms such as urgency, frequency, and bladder or pelvic pain. While the exact causes of IC/BPS remain unclear, it is thought to involve several factors, including abnormalities in the bladder's urothelium, mast cell degranulation within the bladder, inflammation of the bladder, and altered innervation of the bladder. Treatment options include patient education, dietary and lifestyle modifications, medications, intravesical therapy, and surgical interventions. This review article provides insights into IC/BPS, including aspects of treatment, prognosis prediction, and emerging therapeutic options. Additionally, it explores the application of deep learning for diagnosing major diseases associated with IC/BPS.
Files in This Item
There are no files associated with this item.
Appears in
Collections
4. Research institute > Institute of Human Behavior and Genetics > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Altmetrics

Total Views & Downloads

BROWSE